Remove unnecessary columns

import pandas as pd

data = pd.read_csv('metadata.csv')
data = data[data['type'] == 'impedance']
impedance_df = data.drop(['type', 'ambient_temperature', 'test_id', 'uid', 'Capacity', 'filename'], axis=1)
impedance_df.head()

	start_time	battery_id	Re	Rct
1	[2010. 7. 21. 16. 53. ...	B0047	0.05605783343888099	0.20097016584458333
3	[2010 7 21 20 31 5]	B0047	0.05319185850921101	0.16473399914864734
13	[2010. 7. 22. 17. 3. ...	B0047	0.05963791501051059	0.21039872263834902
15	[2010. 7. 22. 20. 40. 25.5]	B0047	0.05512505361624278	0.1754882075917004
17	[2010. 7. 23. 11. 35. ...	B0047	0.058878485312444453	0.19095687096090014

impedance_df['start_time'].count()

1956

Convert values in 'start_time' column to valid datetime objects

import pandas as pd
from datetime import datetime

data = pd.read_csv('metadata.csv')

data = data[data['type'] == 'impedance']

impedance_df = data.drop(['type', 'ambient_temperature', 'test_id', 'uid', 'Capacity', 'filename'], axis=1)

impedance_df['start_time'] = impedance_df['start_time'].astype(str)

impedance_df['start_time'] = impedance_df['start_time'].str.strip("[]").str.replace(",", "")

components = impedance_df['start_time'].str.split()

valid_start_times = []

for component in components:
    if len(component) == 6:
        year = int(float(component[0]))
        month = int(float(component[1]))
        day = int(float(component[2]))
        hour = int(float(component[3]))
        minute = int(float(component[4]))
        second = int(float(component[5]))
        
        valid_start_times.append(datetime(year, month, day, hour, minute, second))
    else:
        valid_start_times.append(pd.NaT)

impedance_df['start_time'] = valid_start_times

impedance_df = impedance_df.dropna(subset=['start_time'])

impedance_df = impedance_df.sort_values(by='start_time')

impedance_df.head()

	start_time	battery_id	Re	Rct
5160	2008-04-18 20:55:29	B0005	0.04466870036616091	0.06945627304536996
5776	2008-04-18 20:55:29	B0007	0.03816813609946085	0.06158094574229446
4544	2008-04-18 20:55:29	B0006	0.06123359021000344	0.0785415394665875
4546	2008-04-18 22:39:16	B0006	0.06561684946388616	0.0886832084583472
5162	2008-04-18 22:39:16	B0005	0.04668700162486933	0.07627474098530587

impedance_df['start_time'].count()

1956

Battery Impedance trend over time graph

import plotly.graph_objects as go

fig = go.Figure()

for unique_battery in impedance_df['battery_id'].unique():
    battery = impedance_df[impedance_df['battery_id'] == unique_battery]
    
    fig.add_trace(go.Scatter(
        x=battery['start_time'],
        y=battery['Re'],
        mode='lines+markers',
        name=f'Resistance (Battery {unique_battery})',
        line=dict(width=1, color='royalblue'),
        marker=dict(size=4, color='darkblue')
    ))

    fig.add_trace(go.Scatter(
        x=battery['start_time'],
        y=battery['Rct'],
        mode='lines+markers',
        name=f'Charge Transfer Resistance (Battery {unique_battery})',
        line=dict(width=1, color='darkorange'),
        marker=dict(size=4, color='red')
    ))

fig.update_layout(
    title="Battery Impedance Trends",
    xaxis_title="Timestamp",
    yaxis_title="Resistance in Ohms",
    xaxis=dict(showgrid=True, tickangle=90),
    yaxis=dict(showgrid=True),
    legend_title="Battery Types",
    template="plotly_white",
    plot_bgcolor="white"
)

fig.show()